Intrusion Detection Systems (titli.ids)

The titli.ids module provides various intrusion detection system (IDS) models for anomaly detection in network traffic.

Public API Overview

All IDS models expose the following 5 public methods:

def train_model(self, train_loader: DataLoader) -> None
    """Train the model on training data."""

def save(self, model_path: Optional[str] = None) -> None
    """Save trained model to disk.

    Args:
        model_path: Path to save model. If None, uses default path:
                   ./artifacts/{dataset_name}/models/{model_name}.pth
    """

def load(self, model_path: Optional[str] = None) -> dict
    """Load trained model from disk.

    Args:
        model_path: Path to load model from. If None, uses default path.

    Returns:
        Checkpoint dictionary with model state
    """

def infer(self, test_loader: DataLoader) -> Tuple[np.ndarray, np.ndarray, np.ndarray]
    """Lightweight inference without metrics computation.

    Args:
        test_loader: PyTorch DataLoader with test data

    Returns:
        Tuple of (y_true, y_pred, reconstruction_errors):
            - y_true: Ground truth labels
            - y_pred: Binary predictions (0=benign, 1=anomaly)
            - reconstruction_errors: Anomaly scores for each sample
    """

def evaluate(self, test_loader: DataLoader) -> None
    """Full evaluation with metrics and visualization.

    Computes F1, Precision, Recall, Accuracy, AUC-ROC and generates:
    - Confusion matrix plot
    - ROC curve plot
    - Anomaly score plot
    - Metrics text file

    All artifacts saved to ./artifacts/{dataset_name}/

    Args:
        test_loader: PyTorch DataLoader with test data
    """

Available Models

Traditional ML Models

LOF (Local Outlier Factor)

OCSVM (One-Class SVM)

Deep Learning Models

Autoencoder

VAE (Variational Autoencoder)

ICL (Instance Contrastive Learning)

Ensemble Models

KitNET

Notes

All models inherit from either BaseSKLearnModel or PyTorchModel (internal base classes)
Methods prefixed with _ are internal and not part of the public API
Default save paths: ./artifacts/{dataset_name}/models/{model_name}.pth
infer() returns: (y_true, y_pred, reconstruction_errors) as numpy arrays
evaluate() generates plots and metrics files in ./artifacts/